用集成学习区分金融诈骗投诉中的诈骗与非诈骗类型
Distinguishing Scams and Fraud with Ensemble Learning
- 构建LLM集成模型，从消费者金融保护局数据中识别诈骗
- 发现大模型在诈骗识别任务中存在误判与泛化不足问题
- 适合关注金融安全、大模型安全应用的研究者
用户越来越多地通过配备大语言模型的网络聊天机器人寻求诈骗防范帮助。消费者金融保护局（CFPB）的投诉数据库是评估大模型在用户诈骗查询场景下表现的丰富数据源，但当前语料库尚未区分诈骗与非诈骗欺诈。我们开发了一种大语言模型集成方法，用于区分CFPB投诉中的诈骗与非诈骗类别，并初步分析了大模型在诈骗防御场景下的优缺点。
原文摘要 · Abstract (English)
Users increasingly query LLM-enabled web chatbots for help with scam defense. The Consumer Financial Protection Bureau's complaints database is a rich data source for evaluating LLM performance on user scam queries, but currently the corpus does not distinguish between scam and non-scam fraud. We developed an LLM ensemble approach to distinguishing scam and fraud CFPB complaints and describe initial findings regarding the strengths and weaknesses of LLMs in the scam defense context.
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